{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['False_',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__array_api_version__',\n",
       " '__array_namespace_info__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_attributes__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numpy_submodules__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_array_api_info',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_expired_attrs_2_0',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_msg',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_specific_msg',\n",
       " '_type_info',\n",
       " '_typing',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfortranarray',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'asmatrix',\n",
       " 'astype',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_count',\n",
       " 'bitwise_invert',\n",
       " 'bitwise_left_shift',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_right_shift',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'char',\n",
       " 'character',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concat',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'cumulative_prod',\n",
       " 'cumulative_sum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'f2py',\n",
       " 'fabs',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isdtype',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issubdtype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'long',\n",
       " 'longdouble',\n",
       " 'longlong',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrix_transpose',\n",
       " 'matvec',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'number',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'permute_dims',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctypeDict',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_printoptions',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'trapezoid',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulong',\n",
       " 'ulonglong',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unique_all',\n",
       " 'unique_counts',\n",
       " 'unique_inverse',\n",
       " 'unique_values',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unstack',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vecdot',\n",
       " 'vecmat',\n",
       " 'vectorize',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date  open  high   low  close  pre_close  change  \\\n",
      "0     601939.SH   20241225  8.78  9.02  8.77   8.88       8.78    0.10   \n",
      "1     601939.SH   20241224  8.60  8.78  8.59   8.78       8.62    0.16   \n",
      "2     601939.SH   20241223  8.45  8.73  8.45   8.62       8.48    0.14   \n",
      "3     601939.SH   20241220  8.47  8.60  8.46   8.48       8.48    0.00   \n",
      "4     601939.SH   20241219  8.52  8.58  8.40   8.48       8.55   -0.07   \n",
      "...         ...        ...   ...   ...   ...    ...        ...     ...   \n",
      "3622  601939.SH   20100108  6.01  6.08  5.99   6.04       6.02    0.02   \n",
      "3623  601939.SH   20100107  6.11  6.15  6.01   6.02       6.11   -0.09   \n",
      "3624  601939.SH   20100106  6.20  6.23  6.10   6.11       6.21   -0.10   \n",
      "3625  601939.SH   20100105  6.15  6.26  6.08   6.21       6.12    0.09   \n",
      "3626  601939.SH   20100104  6.21  6.27  6.12   6.12       6.19   -0.07   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      1.1390  1453468.04  1290884.287  \n",
      "1      1.8561  1294148.26  1128814.466  \n",
      "2      1.6509  1499687.80  1294586.538  \n",
      "3      0.0000   801627.61   682584.497  \n",
      "4     -0.8187   902493.13   765971.927  \n",
      "...       ...         ...          ...  \n",
      "3622   0.3300   564801.55   340188.391  \n",
      "3623  -1.4700  1212983.59   736255.989  \n",
      "3624  -1.6100  1060383.44   654623.387  \n",
      "3625   1.4700  1230221.90   761187.887  \n",
      "3626  -1.1300  1139845.17   705783.462  \n",
      "\n",
      "[3627 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('579f438eb79d5ee41e68586e4adf0dfc11edb58d68b6843e9d843518')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"601939.SH\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date  open  high   low  close  pre_close  change  \\\n",
      "0     601939.SH   20241225  8.78  9.02  8.77   8.88       8.78    0.10   \n",
      "1     601939.SH   20241224  8.60  8.78  8.59   8.78       8.62    0.16   \n",
      "2     601939.SH   20241223  8.45  8.73  8.45   8.62       8.48    0.14   \n",
      "3     601939.SH   20241220  8.47  8.60  8.46   8.48       8.48    0.00   \n",
      "4     601939.SH   20241219  8.52  8.58  8.40   8.48       8.55   -0.07   \n",
      "...         ...        ...   ...   ...   ...    ...        ...     ...   \n",
      "3622  601939.SH   20100108  6.01  6.08  5.99   6.04       6.02    0.02   \n",
      "3623  601939.SH   20100107  6.11  6.15  6.01   6.02       6.11   -0.09   \n",
      "3624  601939.SH   20100106  6.20  6.23  6.10   6.11       6.21   -0.10   \n",
      "3625  601939.SH   20100105  6.15  6.26  6.08   6.21       6.12    0.09   \n",
      "3626  601939.SH   20100104  6.21  6.27  6.12   6.12       6.19   -0.07   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      1.1390  1453468.04  1290884.287  \n",
      "1      1.8561  1294148.26  1128814.466  \n",
      "2      1.6509  1499687.80  1294586.538  \n",
      "3      0.0000   801627.61   682584.497  \n",
      "4     -0.8187   902493.13   765971.927  \n",
      "...       ...         ...          ...  \n",
      "3622   0.3300   564801.55   340188.391  \n",
      "3623  -1.4700  1212983.59   736255.989  \n",
      "3624  -1.6100  1060383.44   654623.387  \n",
      "3625   1.4700  1230221.90   761187.887  \n",
      "3626  -1.1300  1139845.17   705783.462  \n",
      "\n",
      "[3627 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock601939.csv\")\n",
    "stockname='建设银行'\n",
    "stockcode='601939'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3617</th>\n",
       "      <th>3618</th>\n",
       "      <th>3619</th>\n",
       "      <th>3620</th>\n",
       "      <th>3621</th>\n",
       "      <th>3622</th>\n",
       "      <th>3623</th>\n",
       "      <th>3624</th>\n",
       "      <th>3625</th>\n",
       "      <th>3626</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>...</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "      <td>601939.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>8.78</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.31</td>\n",
       "      <td>8.29</td>\n",
       "      <td>8.22</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>9.02</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.65</td>\n",
       "      <td>8.57</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.39</td>\n",
       "      <td>8.34</td>\n",
       "      <td>...</td>\n",
       "      <td>5.91</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.07</td>\n",
       "      <td>6.18</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>8.77</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.44</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.26</td>\n",
       "      <td>8.18</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.85</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>8.88</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.31</td>\n",
       "      <td>...</td>\n",
       "      <td>5.88</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.17</td>\n",
       "      <td>6.14</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>8.78</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.49</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.31</td>\n",
       "      <td>8.21</td>\n",
       "      <td>...</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.86</td>\n",
       "      <td>6.17</td>\n",
       "      <td>6.14</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>1.139</td>\n",
       "      <td>1.8561</td>\n",
       "      <td>1.6509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.8187</td>\n",
       "      <td>0.7067</td>\n",
       "      <td>0.4734</td>\n",
       "      <td>1.8072</td>\n",
       "      <td>-0.1203</td>\n",
       "      <td>1.218</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-5.02</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-1.47</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>1.47</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>1453468.04</td>\n",
       "      <td>1294148.26</td>\n",
       "      <td>1499687.8</td>\n",
       "      <td>801627.61</td>\n",
       "      <td>902493.13</td>\n",
       "      <td>1002938.34</td>\n",
       "      <td>887937.59</td>\n",
       "      <td>1239263.79</td>\n",
       "      <td>1138958.08</td>\n",
       "      <td>1088774.79</td>\n",
       "      <td>...</td>\n",
       "      <td>983743.58</td>\n",
       "      <td>1113058.27</td>\n",
       "      <td>2004459.18</td>\n",
       "      <td>1611011.98</td>\n",
       "      <td>2351778.34</td>\n",
       "      <td>564801.55</td>\n",
       "      <td>1212983.59</td>\n",
       "      <td>1060383.44</td>\n",
       "      <td>1230221.9</td>\n",
       "      <td>1139845.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1290884.287</td>\n",
       "      <td>1128814.466</td>\n",
       "      <td>1294586.538</td>\n",
       "      <td>682584.497</td>\n",
       "      <td>765971.927</td>\n",
       "      <td>859273.151</td>\n",
       "      <td>755171.01</td>\n",
       "      <td>1045535.19</td>\n",
       "      <td>948661.065</td>\n",
       "      <td>900918.867</td>\n",
       "      <td>...</td>\n",
       "      <td>577423.374</td>\n",
       "      <td>655157.343</td>\n",
       "      <td>1193187.793</td>\n",
       "      <td>979282.765</td>\n",
       "      <td>1475780.887</td>\n",
       "      <td>340188.391</td>\n",
       "      <td>736255.989</td>\n",
       "      <td>654623.387</td>\n",
       "      <td>761187.887</td>\n",
       "      <td>705783.462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3627 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0            1            2           3           4     \\\n",
       "ts_code       601939.SH    601939.SH    601939.SH   601939.SH   601939.SH   \n",
       "trade_date     20241225     20241224     20241223    20241220    20241219   \n",
       "open               8.78          8.6         8.45        8.47        8.52   \n",
       "high               9.02         8.78         8.73         8.6        8.58   \n",
       "low                8.77         8.59         8.45        8.46         8.4   \n",
       "close              8.88         8.78         8.62        8.48        8.48   \n",
       "pre_close          8.78         8.62         8.48        8.48        8.55   \n",
       "change              0.1         0.16         0.14         0.0       -0.07   \n",
       "pct_chg           1.139       1.8561       1.6509         0.0     -0.8187   \n",
       "vol          1453468.04   1294148.26    1499687.8   801627.61   902493.13   \n",
       "amount      1290884.287  1128814.466  1294586.538  682584.497  765971.927   \n",
       "\n",
       "                  5          6           7           8           9     ...  \\\n",
       "ts_code      601939.SH  601939.SH   601939.SH   601939.SH   601939.SH  ...   \n",
       "trade_date    20241218   20241217    20241216    20241213    20241212  ...   \n",
       "open              8.52       8.45        8.31        8.29        8.22  ...   \n",
       "high              8.65       8.57        8.55        8.39        8.34  ...   \n",
       "low               8.49       8.44         8.3        8.26        8.18  ...   \n",
       "close             8.55       8.49        8.45         8.3        8.31  ...   \n",
       "pre_close         8.49       8.45         8.3        8.31        8.21  ...   \n",
       "change            0.06       0.04        0.15       -0.01         0.1  ...   \n",
       "pct_chg         0.7067     0.4734      1.8072     -0.1203       1.218  ...   \n",
       "vol         1002938.34  887937.59  1239263.79  1138958.08  1088774.79  ...   \n",
       "amount      859273.151  755171.01  1045535.19  948661.065  900918.867  ...   \n",
       "\n",
       "                  3617        3618         3619        3620         3621  \\\n",
       "ts_code      601939.SH   601939.SH    601939.SH   601939.SH    601939.SH   \n",
       "trade_date    20100115    20100114     20100113    20100112     20100111   \n",
       "open               5.9         5.9          6.0         6.1         6.63   \n",
       "high              5.91        5.94         6.07        6.18         6.63   \n",
       "low                5.8        5.85         5.85        5.99         6.08   \n",
       "close             5.88        5.89         5.86        6.17         6.14   \n",
       "pre_close         5.89        5.86         6.17        6.14         6.04   \n",
       "change           -0.01        0.03        -0.31        0.03          0.1   \n",
       "pct_chg          -0.17        0.51        -5.02        0.49         1.66   \n",
       "vol          983743.58  1113058.27   2004459.18  1611011.98   2351778.34   \n",
       "amount      577423.374  655157.343  1193187.793  979282.765  1475780.887   \n",
       "\n",
       "                  3622        3623        3624        3625        3626  \n",
       "ts_code      601939.SH   601939.SH   601939.SH   601939.SH   601939.SH  \n",
       "trade_date    20100108    20100107    20100106    20100105    20100104  \n",
       "open              6.01        6.11         6.2        6.15        6.21  \n",
       "high              6.08        6.15        6.23        6.26        6.27  \n",
       "low               5.99        6.01         6.1        6.08        6.12  \n",
       "close             6.04        6.02        6.11        6.21        6.12  \n",
       "pre_close         6.02        6.11        6.21        6.12        6.19  \n",
       "change            0.02       -0.09        -0.1        0.09       -0.07  \n",
       "pct_chg           0.33       -1.47       -1.61        1.47       -1.13  \n",
       "vol          564801.55  1212983.59  1060383.44   1230221.9  1139845.17  \n",
       "amount      340188.391  736255.989  654623.387  761187.887  705783.462  \n",
       "\n",
       "[11 rows x 3627 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x256147e3670>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock601939.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3597 entries, 0 to 3596\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3597 non-null   datetime64[ns]\n",
      " 1   open        3597 non-null   float64       \n",
      " 2   high        3597 non-null   float64       \n",
      " 3   low         3597 non-null   float64       \n",
      " 4   close       3597 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.6 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3594</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3595</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3596</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10\n",
       "3592 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368\n",
       "3593 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389\n",
       "3594 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426\n",
       "3595 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467\n",
       "3596 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date  open  high   low  close\n",
      "0    2010-01-04  6.21  6.27  6.12   6.12\n",
      "1    2010-01-05  6.15  6.26  6.08   6.21\n",
      "2    2010-01-06  6.20  6.23  6.10   6.11\n",
      "3    2010-01-07  6.11  6.15  6.01   6.02\n",
      "4    2010-01-08  6.01  6.08  5.99   6.04\n",
      "...         ...   ...   ...   ...    ...\n",
      "3622 2024-12-19  8.52  8.58  8.40   8.48\n",
      "3623 2024-12-20  8.47  8.60  8.46   8.48\n",
      "3624 2024-12-23  8.45  8.73  8.45   8.62\n",
      "3625 2024-12-24  8.60  8.78  8.59   8.78\n",
      "3626 2024-12-25  8.78  9.02  8.77   8.88\n",
      "\n",
      "[3627 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3627 entries, 0 to 3626\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3627 non-null   datetime64[ns]\n",
      " 1   open        3627 non-null   float64       \n",
      " 2   high        3627 non-null   float64       \n",
      " 3   low         3627 non-null   float64       \n",
      " 4   close       3627 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 141.8 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "      <td>6.100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.454</td>\n",
       "      <td>8.368</td>\n",
       "      <td>8.106333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.490</td>\n",
       "      <td>8.389</td>\n",
       "      <td>8.122333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "      <td>8.524</td>\n",
       "      <td>8.426</td>\n",
       "      <td>8.146333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.582</td>\n",
       "      <td>8.467</td>\n",
       "      <td>8.177333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.648</td>\n",
       "      <td>8.534</td>\n",
       "      <td>8.211000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date  open  high   low  close      5     10        30\n",
       "0    2010-01-04  6.21  6.27  6.12   6.12    NaN    NaN       NaN\n",
       "1    2010-01-05  6.15  6.26  6.08   6.21    NaN    NaN       NaN\n",
       "2    2010-01-06  6.20  6.23  6.10   6.11    NaN    NaN       NaN\n",
       "3    2010-01-07  6.11  6.15  6.01   6.02    NaN    NaN       NaN\n",
       "4    2010-01-08  6.01  6.08  5.99   6.04  6.100    NaN       NaN\n",
       "...         ...   ...   ...   ...    ...    ...    ...       ...\n",
       "3622 2024-12-19  8.52  8.58  8.40   8.48  8.454  8.368  8.106333\n",
       "3623 2024-12-20  8.47  8.60  8.46   8.48  8.490  8.389  8.122333\n",
       "3624 2024-12-23  8.45  8.73  8.45   8.62  8.524  8.426  8.146333\n",
       "3625 2024-12-24  8.60  8.78  8.59   8.78  8.582  8.467  8.177333\n",
       "3626 2024-12-25  8.78  9.02  8.77   8.88  8.648  8.534  8.211000\n",
       "\n",
       "[3627 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0           8.78   9.02\n",
       "1           8.60   8.78\n",
       "2           8.45   8.73\n",
       "3           8.47   8.60\n",
       "4           8.52   8.58\n",
       "...          ...    ...\n",
       "3622        6.01   6.08\n",
       "3623        6.11   6.15\n",
       "3624        6.20   6.23\n",
       "3625        6.15   6.26\n",
       "3626        6.21   6.27\n",
       "\n",
       "[3627 rows x 2 columns]>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.12],\n",
       "       [6.21],\n",
       "       [6.11],\n",
       "       ...,\n",
       "       [8.62],\n",
       "       [8.78],\n",
       "       [8.88]], shape=(3627, 1))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.27</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.08</td>\n",
       "      <td>6.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>6.20</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.10</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.99</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3622</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.40</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3624</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.73</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.59</td>\n",
       "      <td>8.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3627 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  open  high   low  close\n",
       "0     14613.0  6.21  6.27  6.12   6.12\n",
       "1     14614.0  6.15  6.26  6.08   6.21\n",
       "2     14615.0  6.20  6.23  6.10   6.11\n",
       "3     14616.0  6.11  6.15  6.01   6.02\n",
       "4     14617.0  6.01  6.08  5.99   6.04\n",
       "...       ...   ...   ...   ...    ...\n",
       "3622  20076.0  8.52  8.58  8.40   8.48\n",
       "3623  20077.0  8.47  8.60  8.46   8.48\n",
       "3624  20080.0  8.45  8.73  8.45   8.62\n",
       "3625  20081.0  8.60  8.78  8.59   8.78\n",
       "3626  20082.0  8.78  9.02  8.77   8.88\n",
       "\n",
       "[3627 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2561c143b80>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.0738 - val_loss: 0.0325\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0112 - val_loss: 0.0015\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0020 - val_loss: 0.0014\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 0.0012\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.5567e-04 - val_loss: 0.0012\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.1596e-04 - val_loss: 0.0012\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.9229e-04 - val_loss: 0.0011\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.7499e-04 - val_loss: 0.0011\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.6433e-04 - val_loss: 0.0011\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.5717e-04 - val_loss: 0.0010\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.5127e-04 - val_loss: 0.0010\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.4669e-04 - val_loss: 0.0010\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.4145e-04 - val_loss: 9.9656e-04\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3696e-04 - val_loss: 0.0010\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3376e-04 - val_loss: 9.9497e-04\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.3008e-04 - val_loss: 9.9256e-04\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2686e-04 - val_loss: 0.0010\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2463e-04 - val_loss: 0.0010\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.2228e-04 - val_loss: 0.0010\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1945e-04 - val_loss: 0.0010\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1617e-04 - val_loss: 0.0010\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1235e-04 - val_loss: 0.0010\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.0776e-04 - val_loss: 0.0010\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.0426e-04 - val_loss: 0.0010\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9968e-04 - val_loss: 0.0010\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9662e-04 - val_loss: 0.0010\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.9293e-04 - val_loss: 0.0010\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8871e-04 - val_loss: 0.0010\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8402e-04 - val_loss: 0.0010\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.8038e-04 - val_loss: 0.0010\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_5 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.03797\n",
      "均方根误差: 0.19486\n",
      "平均绝对误差: 0.15612\n",
      "R2: 0.89033\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
